code: def test_problem_dataset_works(self):
      # We need to derive this class to set the required methods.
      class TestEnv(gym_env_problem.GymEnvProblem):
          name = "TestEnv"
  
          @property
          def input_modality(self):
              return modalities.ModalityType.REAL_L2_LOSS
  
          @property
          def input_vocab_size(self):
              return None
  
          @property
          def target_modality(self):
              return modalities.ModalityType.SYMBOL_WEIGHTS_ALL
  
          @property
          def target_vocab_size(self):
              return 2
  
          @property
          def action_modality(self):
              return modalities.ModalityType.SYMBOL_WEIGHTS_ALL
  
          @property
          def reward_modality(self):
              return modalities.ModalityType.SYMBOL_WEIGHTS_ALL
  
      base_env_name = "CartPole-v0"
      batch_size = 5
      reward_range = (-1, 1)
  
      env = TestEnv(
          base_env_name=base_env_name,
          batch_size=batch_size,
          reward_range=reward_range,
      )
  
      nsteps = 100
      ep, _, _ = self.play_env(env=env, nsteps=nsteps)
  
      # Dump the completed data to disk.
      ep.generate_data(self.tmp_dir, self.tmp_dir)
  
      # Read the actual files and count the trajectories and time-steps.
      dev_filenames = ep.dev_filepaths(
          self.tmp_dir, ep.num_shards[problem.DatasetSplit.EVAL], True
      )
      dev_trajectories, dev_timesteps = self.read_tfrecord_dataset(
          dev_filenames, ep
      )
  
      # Count them using a tf.data.Dataset.
      dev_dataset = ep.dataset(
          tf_estimator.ModeKeys.EVAL, data_dir=self.tmp_dir
      )
  
      last_timestep = -1
      dev_timesteps_ds = 0
      dev_trajectories_ds = 0
      iterator = dev_dataset.make_one_shot_iterator()
      next_element = iterator.get_next()
      with tf.Session() as session:
          while True:
              try:
                  tf_example_dict = session.run(next_element)
  
                  # We have a time-step.
                  dev_timesteps_ds += 1
  
                  this_timestep = tf_example_dict[
                      env_problem.TIMESTEP_FIELD
                  ][
                      0
                  ]  # [0] since every value in tf_example_dict is an array/list.
                  if 1 + last_timestep != this_timestep:
                      dev_trajectories_ds += 1
                      self.assertEqual(0, this_timestep)
                  last_timestep = this_timestep
              except tf.errors.OutOfRangeError:
                  dev_trajectories_ds += 1
                  break
  
      # Make sure that they agree.
      self.assertEqual(dev_trajectories, dev_trajectories_ds)
      self.assertEqual(dev_timesteps, dev_timesteps_ds)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import gym
  from gym.spaces import Box
  from gym.spaces import Discrete
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.envs import env_problem
  from tensor2tensor.envs import env_problem_utils
  from tensor2tensor.envs import gym_env_problem
  from tensor2tensor.layers import modalities
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 10 for calling the GymEnvProblemTest obj's underlying member method code with example usage: obj.test_problem_dataset_works() and returns: modalities, None, modalities, int, modalities, modalities